1. Field of the Invention
The present invention relates generally to the field of medical imaging, and, more particularly, to using local watershed operators for the segmentation of medical structures.
2. Description of the Related Art
Recent technological advances in imaging acquisition devices significantly increase the spatial resolution of image data. For example, a new multi-detector computer tomography (“CT”) machine can produce images with sizes as large as 512×512×1000. Segmentation algorithms for such large images typically need to operate locally to be computationally efficient because a limited amount of time and memory are available.
One method to segment large images is to use a user-defined region of interest. A “region of interest” refers to a selected portion of a two-dimensional (“2D”) or three-dimensional (“3D”) image. While cropping data via a user-defined region of interest may be a salutation for well-localized pathologies, the user-selected regions can still be very large in many applications (e.g., vascular segmentation or bone removal in a computer tomography angiography (“CTA”)). In an alternate method, images can be thresholded to reduce the area in which the segmentation and visualization algorithms need to operate. When an image is “thresholded,” areas of the image are discarded if the intensity of the areas falls outside a pre-defined (e.g., user-defined) value. However, the images are typically thresholded at the expense of removing anatomically important structures from the data.
In medical image analysis, for example, accurate detection of object boundaries is important for quantification reasons, making edge-based algorithms popular. While advances in edge detection algorithms have increased the accuracy and performance of edge detectors, such algorithms are still not robust enough for many practical applications because of the complexity of edge grouping and linking, especially in three dimensions. An exemplary practical application is the segmentation of medical images (e.g., segmentation of vascular structures or the pathologies on the vascular structures).
Unlike edge detection algorithms, watershed transforms produce closed contours and give good performance at junctions and places where the object boundaries are diffused. However, watershed transforms are typically designed for operating on a whole image or cropped image, making such algorithms very slow for large data sets.